Multiplicative Error Modeling Approach for Time Series Forecasting

Poulami Chowdhury; Tanujit Chakraborty

doi:10.20944/preprints202005.0521.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 31 May 2020 / Approved: 31 May 2020 / Online: 31 May 2020 (21:50:01 CEST)

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

Multiplicative error; ARIMA; Neural net

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Multiplicative Error Modeling Approach for Time Series Forecasting

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